The present disclosure relates to a system, components and methodologies for improved cancellation of feedback in a signaling environment having an output and an input, wherein a signal from the output is related to a signal received at the input as feedback. In particular, the present disclosure is directed to a system, components and methodologies that combine the estimation of a plurality of signal sources in an input signal, identification of one of the estimated sources most closely related to the output signal as a feedback signal, and cancellation of the feedback signal from the input signal. The estimation of a plurality of signal sources in the input is called blind signal separation (BSS) because it is performed with no foreknowledge of the real signals that may be combined to form the input signal. The identification and cancellation of the feedback signal from the input signal is called acoustic echo cancellation (AEC) because feedback can produce an echo in an audio signal, and the process cancels the echo. However, the process can be applied to any type of signal, not just signals related to acoustics, and can be used to eliminate any kind of feedback, not just echoes.
Various BSS and AEC methods have been developed in recent decades. In 1960, Bernard Widrow, a professor at Stanford University, and his Ph.D student Ted Hoff developed an algorithm called the Least Mean Square (LMS) algorithm, which is the principle behind echo cancellation. A disadvantage of LMS was that it used adaptive filters to process noisy signals, and the filters could not adapt quickly enough to be useful in real applications. E. Oja and Aapo Hyvarinen developed an algorithm called Fast Independent Component Analysis (Fast ICA) to perform so-called Blind Source Separation (BSS), which involves developing a mixing matrix that represents a plurality of estimated source signals. An advantage was that estimation of the source signals was performed on a set of mixed real signals with no foreknowledge of the signals that were mixed. However, Fast ICA cannot adapt its mixing matrix in a non-stationary environment, i.e., an environment in which various real source signals are starting and stopping, if the source signals change too rapidly. Instead, it requires the assumption that within a single processing frame, the mixing matrix should stay approximately constant. In 1999, J. F. Cardoso developed the so-called joint approximate diagonalization of eigen-matrices (JADE) algorithm for BSS, which also uses a mixing matrix. JADE gives better results than Fast ICA in cases where there are rapid variations in the mixing matrix. Its drawback is the relatively small number of source components that can be estimated from an input signal comprising a plurality of sources, making it inadequate for use in cases comprising a large number of input signal source components. Hence, the JADE algorithm is not very robust. BSS was reported combined with acoustic echo cancellation (AEC).